Representation and Interpretation of Sketches in Mechanical Design: Experimental and Theoretical Approaches
Bibliographic record
Abstract
This paper aims to establish a theoretical foundation for representing and interpreting free-hand design sketches throughout the conceptual design process. Both experimental and theoretical approaches are used. In using the experimental approach, one case study from a book and one case study from an automobile assembly system manufacturer are used to illustrate the characteristics of design sketches. These characteristics provide the requirements for models of sketch representation and interpretation. In using the theoretical approach, a mathematical structure of design sketches is established. This mathematical structure can naturally and logically model the evolving sketches generated in the design process, through integrating the strengths of set theory and mereology. Based on the results of these two approaches, a design sketch language is developed to be a formal foundation of sketch representation and interpretation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".